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Volume 7
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Prediction of Spatiotemporal Distribution of Electric Vehicle Charging Load Considering Transportation Networks and Travel Behaviors

by
Yuansheng Liu
1,
Ke Liu
1,
Yindong Xiao
1,
Yuhang Xie
2 and
Jianbo Yi
2,*
1
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
*
Author to whom correspondence should be addressed.
Vehicles 2025, 7(4), 146; https://doi.org/10.3390/vehicles7040146
Submission received: 5 November 2025 / Revised: 25 November 2025 / Accepted: 27 November 2025 / Published: 30 November 2025
(This article belongs to the Special Issue Next-Generation Power Electronics and Charging Technologies for Electric Vehicles)
Versions Notes

Abstract

As typical dynamic loads, electric vehicles (EVs) introduce significant uncertainty into distribution network operations due to the randomness of their travel behavior and charging demand. To achieve precise spatiotemporal forecasting of charging loads, this paper constructs a multi-dimensional transportation network model that accounts for dynamic road impedance factors and introduces a unit-distance energy consumption calculation method based on road impedance. By integrating the division of urban multifunctional zones and differentiated state-of-charge (SOC) threshold distributions across various EV types, a mapping model between travel chains and charging behaviors is established. Subsequently, large-scale travel and charging events are generated using an origin–destination (OD) probability matrix and Monte Carlo sampling to derive the spatiotemporal distribution of regional EV charging loads. Simulation results for a representative city in southwest China show that the predicted charging loads exhibit a dual-peak pattern, with significant differences across regions and vehicle types, and align well with observed load trends, validating the effectiveness and engineering applicability of the proposed method.
Keywords: electric vehicles; transportation network modeling; trip chain simulation; charging behavior analysis; spatiotemporal load forecasting; Monte Carlo simulation electric vehicles; transportation network modeling; trip chain simulation; charging behavior analysis; spatiotemporal load forecasting; Monte Carlo simulation

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MDPI and ACS Style

Liu, Y.; Liu, K.; Xiao, Y.; Xie, Y.; Yi, J. Prediction of Spatiotemporal Distribution of Electric Vehicle Charging Load Considering Transportation Networks and Travel Behaviors. Vehicles 2025, 7, 146. https://doi.org/10.3390/vehicles7040146

AMA Style

Liu Y, Liu K, Xiao Y, Xie Y, Yi J. Prediction of Spatiotemporal Distribution of Electric Vehicle Charging Load Considering Transportation Networks and Travel Behaviors. Vehicles. 2025; 7(4):146. https://doi.org/10.3390/vehicles7040146

Chicago/Turabian Style

Liu, Yuansheng, Ke Liu, Yindong Xiao, Yuhang Xie, and Jianbo Yi. 2025. "Prediction of Spatiotemporal Distribution of Electric Vehicle Charging Load Considering Transportation Networks and Travel Behaviors" Vehicles 7, no. 4: 146. https://doi.org/10.3390/vehicles7040146

APA Style

Liu, Y., Liu, K., Xiao, Y., Xie, Y., & Yi, J. (2025). Prediction of Spatiotemporal Distribution of Electric Vehicle Charging Load Considering Transportation Networks and Travel Behaviors. Vehicles, 7(4), 146. https://doi.org/10.3390/vehicles7040146

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